tf.random_gamma

tf.random_gamma(
    shape,
    alpha,
    beta=None,
    dtype=tf.float32,
    seed=None,
    name=None
)

Defined in tensorflow/python/ops/random_ops.py.

See the guide: Constants, Sequences, and Random Values > Random Tensors

Draws shape samples from each of the given Gamma distribution(s).

alpha is the shape parameter describing the distribution(s), and beta is the inverse scale parameter(s).

Example:

samples = tf.random_gamma([10], [0.5, 1.5]) # samples has shape [10, 2], where each slice [:, 0] and [:, 1] represents # the samples drawn from each distribution

samples = tf.random_gamma([7, 5], [0.5, 1.5]) # samples has shape [7, 5, 2], where each slice [:, :, 0] and [:, :, 1] # represents the 7x5 samples drawn from each of the two distributions

samples = tf.random_gamma([30], [[1.],[3.],[5.]], beta=[[3., 4.]]) # samples has shape [30, 3, 2], with 30 samples each of 3x2 distributions.

Note: Because internal calculations are done using float64 and casting has floor semantics, we must manually map zero outcomes to the smallest possible positive floating-point value, i.e., np.finfo(dtype).tiny. This means that np.finfo(dtype).tiny occurs more frequently than it otherwise should. This bias can only happen for small values of alpha, i.e., alpha << 1 or large values of beta, i.e., beta >> 1.

Args:

  • shape: A 1-D integer Tensor or Python array. The shape of the output samples to be drawn per alpha/beta-parameterized distribution.
  • alpha: A Tensor or Python value or N-D array of type dtype. alpha provides the shape parameter(s) describing the gamma distribution(s) to sample. Must be broadcastable with beta.
  • beta: A Tensor or Python value or N-D array of type dtype. Defaults to 1. beta provides the inverse scale parameter(s) of the gamma distribution(s) to sample. Must be broadcastable with alpha.
  • dtype: The type of alpha, beta, and the output: float16, float32, or float64.
  • seed: A Python integer. Used to create a random seed for the distributions. See tf.set_random_seed for behavior.
  • name: Optional name for the operation.

Returns:

  • samples: a Tensor of shape tf.concat(shape, tf.shape(alpha + beta)) with values of type dtype.